Overview

Dataset statistics

Number of variables9
Number of observations11065619
Missing cells0
Missing cells (%)0.0%
Duplicate rows629106
Duplicate rows (%)5.7%
Total size in memory844.2 MiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

Dataset has 629106 (5.7%) duplicate rowsDuplicates
voltage is highly overall correlated with current and 2 other fieldsHigh correlation
current is highly overall correlated with voltage and 2 other fieldsHigh correlation
workstation_cpu is highly overall correlated with voltage and 2 other fieldsHigh correlation
workstation_ram is highly overall correlated with voltage and 2 other fieldsHigh correlation
esp32_temperature has 3061149 (27.7%) zerosZeros
workstation_cpu has 6764177 (61.1%) zerosZeros
workstation_gpu has 10793423 (97.5%) zerosZeros
workstation_ram has 6675954 (60.3%) zerosZeros

Reproduction

Analysis started2023-07-25 11:39:32.232655
Analysis finished2023-07-25 11:41:36.099410
Duration2 minutes and 3.87 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

voltage
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.02132
Minimum116.1
Maximum120.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.136926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum116.1
5-th percentile119.6
Q1120
median120.1
Q3120.1
95-th percentile120.5
Maximum120.6
Range4.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.30542714
Coefficient of variation (CV)0.0025447739
Kurtosis16.340703
Mean120.02132
Median Absolute Deviation (MAD)0.1
Skewness-3.4240325
Sum1.3281102 × 109
Variance0.093285736
MonotonicityNot monotonic
2023-07-25T13:41:36.182317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
120 3497786
31.6%
120.1 3420896
30.9%
120.2 1675175
15.1%
119.9 976188
 
8.8%
120.5 579419
 
5.2%
119.6 353741
 
3.2%
119.8 96220
 
0.9%
118.3 86795
 
0.8%
118.9 85433
 
0.8%
120.4 66245
 
0.6%
Other values (33) 227721
 
2.1%
ValueCountFrequency (%)
116.1 3
 
< 0.1%
116.2 3
 
< 0.1%
116.5 1
 
< 0.1%
116.7 1
 
< 0.1%
116.8 4
 
< 0.1%
116.9 5
< 0.1%
117 10
< 0.1%
117.1 4
 
< 0.1%
117.2 7
< 0.1%
117.3 8
< 0.1%
ValueCountFrequency (%)
120.6 788
 
< 0.1%
120.5 579419
 
5.2%
120.4 66245
 
0.6%
120.3 15
 
< 0.1%
120.2 1675175
15.1%
120.1 3420896
30.9%
120 3497786
31.6%
119.9 976188
 
8.8%
119.8 96220
 
0.9%
119.7 12005
 
0.1%

current
Real number (ℝ)

HIGH CORRELATION 

Distinct1092
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82034394
Minimum0.02
Maximum2.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.228711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.13
Q10.68
median0.92
Q30.94
95-th percentile1.057
Maximum2.1
Range2.08
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.23061584
Coefficient of variation (CV)0.28112091
Kurtosis2.1688513
Mean0.82034394
Median Absolute Deviation (MAD)0.11
Skewness-1.421553
Sum9077613.5
Variance0.053183665
MonotonicityNot monotonic
2023-07-25T13:41:36.273556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.92 1561932
14.1%
0.93 1446578
 
13.1%
0.62 1101668
 
10.0%
0.94 813172
 
7.3%
0.13 640347
 
5.8%
0.61 639726
 
5.8%
0.69 411616
 
3.7%
1.03 292740
 
2.6%
1.04 275381
 
2.5%
1.02 257373
 
2.3%
Other values (1082) 3625086
32.8%
ValueCountFrequency (%)
0.02 5995
 
0.1%
0.09 85
 
< 0.1%
0.1 2
 
< 0.1%
0.11 4
 
< 0.1%
0.12 10
 
< 0.1%
0.13 640347
5.8%
0.16 3
 
< 0.1%
0.19 2
 
< 0.1%
0.21 1
 
< 0.1%
0.22 2
 
< 0.1%
ValueCountFrequency (%)
2.1 4
< 0.1%
2.09 3
< 0.1%
2.08 1
 
< 0.1%
1.98 1
 
< 0.1%
1.976 1
 
< 0.1%
1.957 1
 
< 0.1%
1.944 1
 
< 0.1%
1.94 3
< 0.1%
1.93 5
< 0.1%
1.928 2
 
< 0.1%

frequency
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.96468
Minimum59.3
Maximum60.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.311672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum59.3
5-th percentile59.9
Q159.9
median60
Q360
95-th percentile60
Maximum60.3
Range1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.049704949
Coefficient of variation (CV)0.00082890377
Kurtosis-0.7624344
Mean59.96468
Median Absolute Deviation (MAD)0
Skewness-0.82633497
Sum6.635463 × 108
Variance0.002470582
MonotonicityNot monotonic
2023-07-25T13:41:36.345575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
60 7250525
65.5%
59.9 3712301
33.5%
59.8 99556
 
0.9%
60.1 3170
 
< 0.1%
59.7 46
 
< 0.1%
60.2 18
 
< 0.1%
60.3 1
 
< 0.1%
59.3 1
 
< 0.1%
59.4 1
 
< 0.1%
ValueCountFrequency (%)
59.3 1
 
< 0.1%
59.4 1
 
< 0.1%
59.7 46
 
< 0.1%
59.8 99556
 
0.9%
59.9 3712301
33.5%
60 7250525
65.5%
60.1 3170
 
< 0.1%
60.2 18
 
< 0.1%
60.3 1
 
< 0.1%
ValueCountFrequency (%)
60.3 1
 
< 0.1%
60.2 18
 
< 0.1%
60.1 3170
 
< 0.1%
60 7250525
65.5%
59.9 3712301
33.5%
59.8 99556
 
0.9%
59.7 46
 
< 0.1%
59.4 1
 
< 0.1%
59.3 1
 
< 0.1%

energy
Real number (ℝ)

Distinct45882
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.39697
Minimum0
Maximum442.626
Zeros82
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.387231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.46
Q138.31
median119.99
Q3173.52
95-th percentile276.77
Maximum442.626
Range442.626
Interquartile range (IQR)135.21

Descriptive statistics

Standard deviation98.100056
Coefficient of variation (CV)0.80149087
Kurtosis1.4894105
Mean122.39697
Median Absolute Deviation (MAD)72.76
Skewness1.1365584
Sum1.3543983 × 109
Variance9623.6209
MonotonicityNot monotonic
2023-07-25T13:41:36.430646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137.1 7730
 
0.1%
137.43 7723
 
0.1%
136.99 7720
 
0.1%
137.24 7719
 
0.1%
137.29 7719
 
0.1%
136.93 7718
 
0.1%
137.07 7717
 
0.1%
136.6 7716
 
0.1%
136.85 7715
 
0.1%
136.49 7714
 
0.1%
Other values (45872) 10988428
99.3%
ValueCountFrequency (%)
0 82
 
< 0.1%
0.02 570
< 0.1%
0.03 687
< 0.1%
0.04 678
< 0.1%
0.05 760
< 0.1%
0.06 704
< 0.1%
0.07 638
< 0.1%
0.08 651
< 0.1%
0.09 759
< 0.1%
0.1 686
< 0.1%
ValueCountFrequency (%)
442.626 13
< 0.1%
442.625 31
< 0.1%
442.624 25
< 0.1%
442.623 31
< 0.1%
442.622 26
< 0.1%
442.621 29
< 0.1%
442.62 31
< 0.1%
442.619 25
< 0.1%
442.618 30
< 0.1%
442.617 25
< 0.1%

power_factor
Real number (ℝ)

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86025335
Minimum0
Maximum1
Zeros6074
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.474963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32
Q10.86
median0.89
Q30.9
95-th percentile0.97
Maximum1
Range1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.13945405
Coefficient of variation (CV)0.16210811
Kurtosis10.851852
Mean0.86025335
Median Absolute Deviation (MAD)0.03
Skewness-3.4260506
Sum9519235.8
Variance0.019447431
MonotonicityNot monotonic
2023-07-25T13:41:36.521094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.86 3253719
29.4%
0.9 2399702
21.7%
0.89 1296058
 
11.7%
0.94 678932
 
6.1%
0.32 609354
 
5.5%
0.97 538312
 
4.9%
0.85 391609
 
3.5%
0.95 361836
 
3.3%
0.87 328359
 
3.0%
0.91 299376
 
2.7%
Other values (47) 908362
 
8.2%
ValueCountFrequency (%)
0 6074
0.1%
0.03 4
 
< 0.1%
0.04 1
 
< 0.1%
0.07 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 2
 
< 0.1%
0.21 1
 
< 0.1%
0.27 1
 
< 0.1%
ValueCountFrequency (%)
1 903
 
< 0.1%
0.99 303
 
< 0.1%
0.98 35033
 
0.3%
0.97 538312
4.9%
0.96 156648
 
1.4%
0.95 361836
3.3%
0.94 678932
6.1%
0.93 298199
2.7%
0.92 248311
 
2.2%
0.91 299376
2.7%

esp32_temperature
Real number (ℝ)

ZEROS 

Distinct87
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.100912
Minimum0
Maximum53.3333
Zeros3061149
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.564630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.67
Q333.89
95-th percentile53.3333
Maximum53.3333
Range53.3333
Interquartile range (IQR)33.89

Descriptive statistics

Standard deviation15.987543
Coefficient of variation (CV)0.66335841
Kurtosis-0.83834116
Mean24.100912
Median Absolute Deviation (MAD)4.45
Skewness-0.47949705
Sum2.6669151 × 108
Variance255.60153
MonotonicityNot monotonic
2023-07-25T13:41:36.610630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3061149
27.7%
33.89 1957975
17.7%
33.33 1318189
11.9%
34.44 669242
 
6.0%
53.3333 660831
 
6.0%
26.67 580106
 
5.2%
27.22 517443
 
4.7%
32.78 413152
 
3.7%
26.11 358328
 
3.2%
32.22 335751
 
3.0%
Other values (77) 1193453
 
10.8%
ValueCountFrequency (%)
0 3061149
27.7%
20.5556 1
 
< 0.1%
20.56 1
 
< 0.1%
21.1111 1
 
< 0.1%
21.6667 2
 
< 0.1%
21.67 13
 
< 0.1%
22.22 774
 
< 0.1%
22.2222 3
 
< 0.1%
22.78 23626
 
0.2%
23.33 21378
 
0.2%
ValueCountFrequency (%)
53.3333 660831
6.0%
52.7778 51
 
< 0.1%
52.2222 74
 
< 0.1%
51.6667 56
 
< 0.1%
51.1111 55
 
< 0.1%
50.5556 64
 
< 0.1%
50 61
 
< 0.1%
49.4444 42
 
< 0.1%
48.8889 61
 
< 0.1%
48.3333 51
 
< 0.1%

workstation_cpu
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2696
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9972231
Minimum0
Maximum100
Zeros6764177
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.656308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.43
95-th percentile8.85
Maximum100
Range100
Interquartile range (IQR)4.43

Descriptive statistics

Standard deviation3.3134864
Coefficient of variation (CV)1.6590467
Kurtosis8.2015163
Mean1.9972231
Median Absolute Deviation (MAD)0
Skewness2.0132835
Sum22100510
Variance10.979192
MonotonicityNot monotonic
2023-07-25T13:41:36.703139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6764177
61.1%
0.1 187572
 
1.7%
0.2 177318
 
1.6%
0.3 145380
 
1.3%
4.43 129114
 
1.2%
4.3 101019
 
0.9%
0.4 98748
 
0.9%
4.56 90528
 
0.8%
4.36 88841
 
0.8%
4.49 82023
 
0.7%
Other values (2686) 3200899
28.9%
ValueCountFrequency (%)
0 6764177
61.1%
0.1 187572
 
1.7%
0.2 177318
 
1.6%
0.26 1
 
< 0.1%
0.3 145380
 
1.3%
0.4 98748
 
0.9%
0.5 52733
 
0.5%
0.6 29489
 
0.3%
0.7 11675
 
0.1%
0.8 8304
 
0.1%
ValueCountFrequency (%)
100 6
< 0.1%
99.8 5
< 0.1%
99.62 1
 
< 0.1%
99.09 1
 
< 0.1%
98.6 4
< 0.1%
97.53 1
 
< 0.1%
96.74 1
 
< 0.1%
96.61 1
 
< 0.1%
96.22 2
 
< 0.1%
95.44 1
 
< 0.1%

workstation_gpu
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.035774151
Minimum0
Maximum63
Zeros10793423
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:36.973826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31441047
Coefficient of variation (CV)8.7887611
Kurtosis928.91307
Mean0.035774151
Median Absolute Deviation (MAD)0
Skewness19.8574
Sum395863.13
Variance0.098853944
MonotonicityNot monotonic
2023-07-25T13:41:37.018638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10793423
97.5%
1 179570
 
1.6%
2 33668
 
0.3%
3 18130
 
0.2%
0.01 17904
 
0.2%
4 7229
 
0.1%
5 4581
 
< 0.1%
0.02 2359
 
< 0.1%
6 2194
 
< 0.1%
0.03 1987
 
< 0.1%
Other values (42) 4574
 
< 0.1%
ValueCountFrequency (%)
0 10793423
97.5%
0.01 17904
 
0.2%
0.02 2359
 
< 0.1%
0.03 1987
 
< 0.1%
0.04 666
 
< 0.1%
0.05 285
 
< 0.1%
0.06 142
 
< 0.1%
0.07 149
 
< 0.1%
0.08 81
 
< 0.1%
0.09 48
 
< 0.1%
ValueCountFrequency (%)
63 1
 
< 0.1%
57 1
 
< 0.1%
40 2
 
< 0.1%
34 1
 
< 0.1%
30 4
< 0.1%
29 3
 
< 0.1%
28 7
< 0.1%
26 7
< 0.1%
25 8
< 0.1%
24 3
 
< 0.1%

workstation_ram
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3560
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.779082
Minimum0
Maximum55.2
Zeros6675954
Zeros (%)60.3%
Negative0
Negative (%)0.0%
Memory size168.8 MiB
2023-07-25T13:41:37.065910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.21
95-th percentile44.11
Maximum55.2
Range55.2
Interquartile range (IQR)37.21

Descriptive statistics

Standard deviation18.691217
Coefficient of variation (CV)1.2647075
Kurtosis-1.5639621
Mean14.779082
Median Absolute Deviation (MAD)0
Skewness0.55196455
Sum1.635397 × 108
Variance349.36159
MonotonicityNot monotonic
2023-07-25T13:41:37.111405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6675954
60.3%
42.5 25973
 
0.2%
42.7 22949
 
0.2%
47.5 21099
 
0.2%
44.3 20924
 
0.2%
47.4 20756
 
0.2%
38.95 20305
 
0.2%
38.96 20194
 
0.2%
45.9 20136
 
0.2%
42.3 20102
 
0.2%
Other values (3550) 4197227
37.9%
ValueCountFrequency (%)
0 6675954
60.3%
11.37 1
 
< 0.1%
11.38 40
 
< 0.1%
11.39 29
 
< 0.1%
11.4 161
 
< 0.1%
11.41 62
 
< 0.1%
11.42 15
 
< 0.1%
11.43 56
 
< 0.1%
11.44 21
 
< 0.1%
11.45 45
 
< 0.1%
ValueCountFrequency (%)
55.2 4
 
< 0.1%
55.1 10
< 0.1%
55 17
< 0.1%
54.9 21
< 0.1%
54.8 4
 
< 0.1%
54.7 10
< 0.1%
54.6 8
 
< 0.1%
54.5 13
< 0.1%
54.3 12
< 0.1%
54.2 14
< 0.1%

Interactions

2023-07-25T13:41:15.314758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:29.397514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:35.252736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:40.909873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:46.684114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:52.317020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:58.073167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:03.565626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:09.240038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:15.994400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:30.019137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:35.801622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:41.539318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:47.293425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:52.981396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:58.668899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:04.175989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:09.904508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:16.665596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:30.628514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:36.424172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:42.138671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:47.916370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:53.625033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:59.265892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:04.792039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:10.596795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:17.324043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:31.228221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:37.030712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:42.771508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:48.506210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:54.234637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:59.862422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:05.393576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:11.326368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:18.025392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:32.082730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:37.652401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:43.407834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:49.132442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:54.813121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:00.466244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:06.013022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:11.994185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:18.764829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:32.691801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:38.261870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:44.039985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:49.740515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:55.415393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:01.013382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:06.666613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:12.682925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:19.438900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:33.293553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:38.870063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:44.683205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:50.349177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:56.200052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:01.627669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:07.223147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:13.384977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:20.114849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:33.947290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:39.555843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:45.380441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:50.994053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:56.847690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:02.286802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:07.918339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:13.946316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:20.699068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:34.598269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:40.218218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:46.064181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:51.699787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:40:57.481111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:02.946630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:08.567961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-25T13:41:14.649512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-25T13:41:37.148329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
voltage1.000-0.8210.1210.008-0.1880.152-0.606-0.133-0.567
current-0.8211.000-0.006-0.045-0.028-0.2980.5720.0920.598
frequency0.121-0.0061.0000.0180.0080.0140.0060.0050.011
energy0.008-0.0450.0181.0000.4640.3940.4410.0900.449
power_factor-0.188-0.0280.0080.4641.0000.3720.4360.1360.262
esp32_temperature0.152-0.2980.0140.3940.3721.0000.2270.1590.220
workstation_cpu-0.6060.5720.0060.4410.4360.2271.0000.2640.886
workstation_gpu-0.1330.0920.0050.0900.1360.1590.2641.0000.190
workstation_ram-0.5670.5980.0110.4490.2620.2200.8860.1901.000

Missing values

2023-07-25T13:41:20.843774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-25T13:41:24.405484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
fecha_servidor
2021-05-05 22:05:27119.91.1560.00.00.920.00.00.00.0
2021-05-05 22:05:28119.91.1560.00.00.920.00.00.00.0
2021-05-05 22:05:28119.91.0960.00.00.910.00.00.00.0
2021-05-05 22:05:29119.91.0960.00.00.910.00.00.00.0
2021-05-05 22:05:29120.01.0160.00.00.890.00.00.00.0
2021-05-05 22:05:30120.01.0160.00.00.890.00.00.00.0
2021-05-05 22:05:30120.00.9660.00.00.870.00.00.00.0
2021-05-05 22:05:31120.00.9660.00.00.870.00.00.00.0
2021-05-05 22:05:32120.01.0060.00.00.880.00.00.00.0
2021-05-05 22:05:33120.00.9760.00.00.880.00.00.00.0
voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram
fecha_servidor
2021-12-04 08:18:08118.80.70159.924.8500.9653.33335.210.024.95
2021-12-04 08:18:08119.61.04759.9442.6260.8953.333313.690.048.56
2021-12-04 08:18:08119.61.03159.9442.6260.9053.33337.540.048.55
2021-12-04 08:18:09118.80.70759.924.8500.9753.33335.260.024.95
2021-12-04 08:18:09119.61.15260.0442.6260.9153.33335.770.048.55
2021-12-04 08:18:10118.80.71259.924.8500.9753.33335.080.024.95
2021-12-04 08:18:11118.90.67559.924.8500.9853.33335.210.024.94
2021-12-04 08:18:11119.61.18959.9442.6260.9253.333313.180.048.58
2021-12-04 08:18:11119.61.15259.9442.6260.9153.333313.180.048.58
2021-12-04 08:18:12118.80.69159.924.8500.9753.33334.430.024.95

Duplicate rows

Most frequently occurring

voltagecurrentfrequencyenergypower_factoresp32_temperatureworkstation_cpuworkstation_gpuworkstation_ram# duplicates
628438120.50.1360.0136.620.3234.440.00.00.04669
628457120.50.1360.0136.650.3234.440.00.00.04543
628525120.50.1360.0136.770.3234.440.00.00.04316
628445120.50.1360.0136.630.3234.440.00.00.04277
628520120.50.1360.0136.760.3234.440.00.00.04213
628801120.50.1360.0137.120.3233.890.00.00.04210
628726120.50.1360.0137.020.3234.440.00.00.04150
628462120.50.1360.0136.660.3234.440.00.00.04099
628838120.50.1360.0137.210.3233.890.00.00.04081
628536120.50.1360.0136.790.3233.890.00.00.04074